2 citations · 3 across the 6 of their papers we have counts for
7 papers
Algebraic statistics of Hüsler-Reiss graphical models in multivariate extremes
Carlos Améndola, Jane Ivy Coons, Alexandros Grosdos +1
The field of extreme value statistics is concerned with modeling and predicting rare events. In a Hüsler-Reiss graphical model, a graph represents extremal conditional independence…
Algebraic geometry of rational neural networks
Alexandros Grosdos, Elina Robeva, Maksym Zubkov
We study the expressivity of rational neural networks (RationalNets) through the lens of algebraic geometry. We consider rational functions that arise from a given RationalNet to b…
Identifiability through special linear measurements
Fulvio Gesmundo, Alexandros Grosdos, André Uschmajew
We show that one can always identify a point on an algebraic variety uniquely with generic linear measurements taken themselves from a variety under minimal assumpt…
Rational Maximum Likelihood Estimators of Kronecker Covariance Matrices
Mathias Drton, Alexandros Grosdos, Andrew McCormack
As is the case for many curved exponential families, the computation of maximum likelihood estimates in a multivariate normal model with a Kronecker covariance structure is typical…
Algebraic Sparse Factor Analysis
Mathias Drton, Alexandros Grosdos, Irem Portakal +1
Factor analysis is a statistical technique that explains correlations among observed random variables with the help of a smaller number of unobserved factors. In traditional full f…
Parametric and nonparametric symmetries in graphical models for extremes
Frank Röttger, Jane Ivy Coons, Alexandros Grosdos
Colored graphical models provide a parsimonious approach to modeling high-dimensional data by exploiting symmetries in the model parameters. In this work, we introduce the notion o…
Third-Order Moment Varieties of Linear Non-Gaussian Graphical Models
Carlos Améndola, Mathias Drton, Alexandros Grosdos +2
In this paper we study linear non-Gaussian graphical models from the perspective of algebraic statistics. These are acyclic causal models in which each variable is a linear combina…